| Citation: |
Yuan Tian, Yang-landuo Deng, Ming-zhi Zhang, Xiao Pang, Rui-ping Ma, Jian-xue Zhang, 2024. Short-term displacement prediction for newly established monitoring slopes based on transfer learning, China Geology, 7, 350-363. doi: 10.31035/cg2024053
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Short-term displacement prediction for newly established monitoring slopes based on transfer learning
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Abstract
This study makes a big progress in dealing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program, an unprecedented disaster mitigation program in China, where lots of newly established monitoring slopes lack sufficient historical deformation data, making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards. A slope displacement prediction method based on transfer learning is therefore proposed. Initially, the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data, thus enabling rapid and efficient predictions for these slopes. Subsequently, as time goes on and monitoring data accumulates, fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy, enabling continuous optimization of prediction results. A case study indicates that, after being trained on a multi-slope integrated dataset, the TCN-Transformer model can efficiently serve as a pre-trained model for displacement prediction at newly established monitoring slopes. The three-day average RMSE is significantly reduced by 34.6% compared to models trained only on individual slope data, and it also successfully predicts the majority of deformation peaks. The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%, showcasing great improvement in predictive accuracy. In conclusion, taking advantage of transfer learning, the proposed slope displacement prediction method effectively utilizes the available data, which enables the rapid deployment and continuous improvement of displacement predictions on newly established monitoring slopes.
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References
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Yuan Tian, Yang-landuo Deng, Ming-zhi Zhang, Xiao Pang, Rui-ping Ma, Jian-xue Zhang, 2024. Short-term displacement prediction for newly established monitoring slopes based on transfer learning, China Geology, 7, 350-363. doi: 10.31035/cg2024053
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Figure 1.
Distribution map of landslide monitoring sites.
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Figure 2.
Overview of transfer learning-based slope displacement prediction method.
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Figure 3.
Generation of sample set using the sliding window method (after Lu H et al., 2023).
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Figure 4.
TCN-Transformer for short-term slope displacement predictions (after Tian Y et al., 2023).
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Figure 5.
Pre-trained model parameter transfer method (after Lemley J et al., 2017).
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Figure 6.
$ {R}^{2} $ box diagram for 3-day displacement prediction on the test set. -
Figure 7.
Three-day prediction results of Yinjiaao slope by pre-trained model. a‒Day 1; b‒Day 2; c‒Day 3.
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Figure 8.
Three-day prediction results of Yingshang Kaziping slope by pre-trained model. a‒Day 1; b‒Day 2; c‒Day 3.
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Figure 9.
Experiment data for fine-tuning.
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Figure 10.
Single slope model fine-tuning loss curve. a‒Fine-tune the attention and output layers; b‒Fine-tune the output layer only.
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Figure 11.
Displacement prediction results of Yingshang Kaziping slope. a‒Day 1; b‒Day 2; c‒Day 3.
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Figure 12.
Attention distribution of single-slope fine-tuning model.
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Figure 13.
Distribution of attention with time distance of single-slope fine-tuning model.